Using Artificial Intelligence Resources in Dialysis and Kidney Transplant Patients: A Literature Review

Table 2

Different types of AI algorithms used in G5D/T trials.

Type of AI/ML algorithm used	No.	Studies	Ref.
Unspecified machine learning (ML) algorithms	15	Cadena 2010, Fuertinger 2013, Barbieri 2015, Barbieri 2016, Brier 2016, Saadat 2017, Bhatia 2018, Bucalo 2018, Usvyat 2018, Zhang 2017, John 2019, Decruyenaere 2015, Karademirci 2015, Tang 2017, Gallon 2018	[27, 28, 35, 80]; [16, 42, 82, 83]; [48, 49, 75, 84]; [53, 55, 85]
ML—Naive Bayes models	1	Rodrigues 2017	[50]
ML—support vector machine (SVM)	3	Martin-Guerrero 2003, Chao 2018, Fernandez-Lozano 2018	[30, 43, 51]
ML—-NN (-nearest neighbor)	1	Fernandez-Lozano 2018	[51]
ML—reinforcement learning with Markov decision processes (MDP)	1	Escandell-Montero 2014	[26]
Fuzzy
Fuzzy logic	5	Nordio 1994, Nordio 1995, Mancini 2007, Gaweda 2008, Zhang 2005	[21, 22, 34, 79]; [44]
Coactive fuzzy	1	Chen 2007	[37]
Fuzzy Petri nets	1	Chen 2014	[41]
ML—natural language processing	1	Nigwekar 2014	[40]
Data mining	4	Bellazzi 2005, Brito 2019, Srinivas 2017, Jia 2018	[13, 47, 60, 86]
Bayesian belief network	1	Brown 2012	[59]
Dynamic time warping (DTW)	1	Fritsche 2002	[56]
ML—unspecified neural network (NN) algorithm	30	Guh 1998, Akl 2001, Goldfarb-Rumyantzev 2003, Martin Guerrero 2003, Gabutti 2004, Gabutti 2004, Chiu 2005, Fernandez 2005, Gabutti 2006, Tangri 2006, Wang 2006, Gaweda 2008, Bhan 2010, Jacob 2010, Azar 2011, Barbieri 2016, Brier 2018, Niel 2018, Barbieri 2019, Hueso 2019, Chen 2006, Tangri 2011, Simic-Ogrizovic 1999	[31, 76–78]; [19, 20, 23, 88]; [14, 32, 33, 36]; [15, 29, 38, 81]; [7, 18, 87, 89]; [45, 46, 54, 65]
ML—multilayer perceptron		Stachowska 2006, Santori 2007, Sharma 2008, Tang 2017	[25, 52, 55, 57]
(MLP) ML—recurrent NN		Niel 2018, Rashidi Khazaee 2018, Thishya 2018	[62, 64]
	1	Martin-Guerrero 2003	[31]
	1	Gallon 2018	[85]
ML—tree-based modeling (TBM)
Random forest (RF)	7	Titapiccolo 2013, Rodriguez 2016, Fernandez-Lozano 2018, Sharma 2008, Greco 2010, Tang 2017, Yoo 2017	[17, 39, 51, 52]; [55, 58, 61]
Decision trees	3	Goldfarb-Rumyantzev 2003, Raghavan 2005, Yoo 2017	[61, 74, 78]
Conditional inference trees	1	Seeling 2012	[63]